Research on the Cross-Industry Application of Autonomous Driving Technology in the Field of FinTech

This thesis focuses on the interdisciplinary integration of autonomous driving technology and financial technology (FinTech), exploring the synergistic effects and application prospects of these two cutting-edge fields under the impetus of digitalization and intelligence. With continuous breakthroughs in artificial intelligence, big data, and the Internet of Things, autonomous driving technology has gradually transitioned from laboratory research to commercial applications, encompassing core technologies such as environmental perception, decision-making algorithms, and execution systems. Concurrently, FinTech aims to revolutionize traditional financial business models by leveraging blockchain, cloud computing, and intelligent risk control to upgrade services like payments, loans, and insurance. This paper seeks to investigate the multifaceted application scenarios of autonomous driving technology within the FinTech domain, analyze the technical, data security, regulatory, and business model challenges arising from their convergence, and propose practical countermeasures to provide theoretical and practical guidance for policy formulation and corporate strategy. Initially, the thesis reviews the developmental trajectories and core technologies of both autonomous driving and FinTech. From the perspective of autonomous driving, recent advancements in deep learning and sensor fusion have enabled vehicles to achieve high-precision environmental perception and real-time decision-making. In parallel, FinTech has made significant strides in areas like risk management, credit assessment, and robo-advisory by harnessing big data analytics, blockchain, and artificial intelligence. Through a systematic review of domestic and international literature, this paper synthesizes the theoretical foundations of their interdisciplinary integration, constructing a comprehensive analytical framework based on theories of technological innovation diffusion and ecosystem development. Subsequently, employing methods such as literature review, case analysis, and a combination of qualitative and quantitative approaches, the thesis explores specific application scenarios where autonomous driving technology intersects with FinTech. Furthermore, the thesis delves into the primary challenges encountered during the interdisciplinary application of autonomous driving technology and FinTech. Foremost is data security and privacy protection; data generated by autonomous vehicles encompasses sensitive information such as personal privacy, driving routes, and habits. Ensuring information security and compliance during data sharing and cross-platform applications is imperative. Additionally, technical integration and standardization pose significant challenges, as disparities in data formats, interface protocols, and system compatibility exist between autonomous driving systems and FinTech platforms. Establishing unified technical standards and interface specifications is essential for seamless system integration. Regulatory and policy hurdles are also prominent; both fields are rapidly evolving, and existing regulations may lag, affecting business compliance and promotion. Lastly, market acceptance and business model innovation are critical; consumer awareness and trust in the application of autonomous driving data in financial services, along with the ability of financial institutions to design competitive products leveraging technological advantages, directly influence market adoption.